WHAT IS BIG DATA?

Big data refers to the ever-growing volume of data, increasing velocity in the generation of that data,

and increased variety of types of data. In 2016, adoption of big data skyrocketed across all sizes of

businesses. And it’s not just IT teams that are looking to take advantage of all of that information.

Business users want access to these data sets as well. There really is no one concrete number that

describes how big data is. The big data environment continues to grow more complex as the volume,

variety and velocity of data increases. New technologies have emerged, improving how data is stored,

managed, and retrieved.

5 STEPS FOR BUILDING A BIG DATA STRATEGY

Understanding of big data, the step is to build out a plan to deal with it. Get started on your big data

strategy with these five easy steps:

1. UNDERSTAND YOUR BUSINESS GOALS

First, identify the business problem or case your organization is looking to address and map it to

the right benchmarks, metrics, and KPIs. For example, is your goal to optimize operational

levels? Increase sales forecast transparency? Or monitor the performance of equipment across

regional locations? Insights into big data can help your business achieve all of these objectives

and much more. Big data also gives IT and the line of business an unprecedented opportunity to

work together to increase productivity, efficiency, and business processes. By increasing

accountability and collaboration across the business – along with clearly outlining requirements

and priorities – you will best position your company to uncover the hidden value in your data.

2. HAVE A CLEAR STRATEGY

It’s important to be strategic in your implementation of big data technology so you can make

the most of your existing IT infrastructure and prevent the new technology from becoming a

siloed part of your organization. For instance, if you decide to move to Hadoop, then you need

to subsequently choose a distribution player so you can deploy it. And, you need to select a big

data analytics platform that can transform the raw data you put into Hadoop into real-time

insights for the organization. Analytics’ end-to-end platform enables you to run analyses across

your company’s data – transactions, customer interactions, and machine data.

3. SELECT THE RIGHT PLATFORM

When selecting a big data analytics platform, ask yourself if it has the following attributes:

• The ability to gain insights from multi-structured data

• Tools that show you all of your data, not just what’s at the top of the iceberg

• Freedom from IT – the ability to ask the questions you want, when you want

• Fast answers, regardless of how much data you have on hand

• Access to big data for everyone – not just users with “scientist” in their title

• Tools built natively so the business can make the most of the data

4. START SMALL AND MEASURE

Once you have the ability to access and analyze information, the temptation to go big and

analyze all the data in sight is hard to resist. Instead, be strategic. Pick one business problem,

perform an audit to understand what data you need, and then measure that particular set of

data for insights. Focus on small wins first, as this will help all employees fully understand the

data in their everyday work. This strategy will also enable you to build the momentum to change

your organization into a data-driven enterprise.

5. BUILD A DATA-DRIVEN CULTURE

When users feel empowered to ask questions of big data, companies can build a data-driven

culture fostered by collaboration and innovation. With self-service analytics…

• Users can examine data from every touch point – from transactions to social posts – and

make informed decisions faster

• The power and flexibility to get answers to questions is much easier, and groups can

easily share that information with others

• Data scientists can make their work more accessible to the organization, which makes

what they do more meaningful to the business

• IT professionals can stop worrying about the volume, variety, and velocity of data;

whether users have access to the data they need; and whether or not that data is secure

THE 4 V’S OF BIG DATA

V IS FOR VOLUME

(the amount of data) While one number cannot

characterize big data, a few

interesting ones are worth

noting. Recent studies predict

there will be 40 zettabytes, or a

trillion gigabytes, of data

generated in 2020 –which is 300

times that in the year 2005.

Regardless, big data can be an

issue specifically for those users

who hoard every one of their

email messages, take loads of

pictures, and record video after

video. What happens when

these users run out of disk

space? With this in mind, big

data becomes a concept that

applies on a more personal level,

and scaling it then becomes

difficult on many different levels.

V IS FOR VELOCITY (the speed of data change)

Consider the billion pieces of

content that are shared on

Facebook every day. In London,

an estimated more than 6

million closed-circuit camera TVs

are capturing video on a daily

basis. Each video is captured at

30 frames per second, which

equates to roughly 100 million

frames per second in total –

that’s over 15 trillion frames per

day! In Major League Baseball, a

system in every stadium

captures the movement of the

players and the ball on the field

using advanced video and radar.

This system generates

approximately seven terabytes

of data per game. That’s a lot of

data that must be turned around

for real-time analysis during

each and every event. The

analytics challenges presented

by this velocity of data

demonstrate that data is not just

coming from business

applications anymore. It’s

coming from everywhere!

V IS FOR VARIETY

(the different forms of data)

Data comes in many forms.

Whether it’s text, images,

audio, or video - the channels

they feed into can be easily

distractible and hard to

decipher. Now some of this

data is unstructured, which

means it isn’t ready to be

conventionally processed and

analyzed. But even when the

data is structured, the fact

that it comes from different

places ultimately means each

piece of data may have a

different structure. Within

the realm of business

applications, resolving such

data inconsistencies across

changing systems must be

addressed, whether through

sales analytics tools,

marketing tools, finance, HR,

or ERP systems.

V IS FOR VALUE

(the value of data) Information about a

transaction has become even

more valuable than the

transaction itself. For

example, as a retailer, you

want to know the sequence

of events that leads to a

transaction (what marketing

campaign worked, the

customer’s click path on the

website, and so on). All of this

information can help build

value by driving more

transactions and building

stronger relationships with

customers. But value is never

a straightforward path; you

often won’t know how some

of the data you have today

can help you answer a

question tomorrow.